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StateGraph

As explained in Knowledge Graphs and LLMs in Action

StateGraph is a LangGraph class for constructing workflows whose nodes operate over shared typed state.

StateGraph is a LangGraph class for constructing workflows whose nodes operate over shared typed state. AgentState supplies type safety while nodes represent processing steps and edges define execution order. section 15.1.5

A StateGraph pipeline can:

  • Add agent functions as named nodes. section 15.1.5
  • Connect nodes with sequential edges. section 15.1.5
  • Route dynamically after execution, retrying failures, summarizing graph results, or ending directly. section 15.1.5

Knowledge-graph QA pipeline

A workflow detects intent, extracts a Neo4j schema, converts text to Cypher, executes the query, and conditionally retries, summarizes, or completes. Each node reads and returns fields in AgentState. section 15.1.5

Figure 15.7 LangGraph implementation of the knowledge expert-emulating graph-querying pipeline
figure

Diagram the expert-emulating graph-querying pipeline and its connected agent stages. section 15.1.5

How Knowledge Graphs and LLMs in Action covers it

How deep it goes
Defines initialization, node registration, sequential edges, and conditional routing, but does not detail StateGraph internals.
Examples
Uses intent detection, schema extraction, text-to-Cypher conversion, query execution, retry routing, and summary generation.
What it assumes
Assumes familiarity with agent functions, shared state, Neo4j queries, and sequential workflow control.
What you can do after
Build it from scratch as a named-node pipeline with typed shared state and error-aware conditional execution.
How it connects
Connects AgentState, LangGraph, agent nodes, conditional edges, Neo4j, Cypher, retries, and summarization.

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This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.